Abstract
Today with the help of emerging technologies scientists can gather data in many sectors which includes the Healthcare sector. The amount of data generated by machines and humans is overwhelming and is growing faster than it has ever had before. Taking advantage of all the power that exist in that data we have come up with a novel idea where we have used the development of machine learning techniques in diagnosing cardiovascular disease. Here, we fit a function to examples and using the function to generalize and make predictions about new diagnosis. In other words, the machine learning models learn from past data to make predictions about a patient diagnosis. This research provides a detailed work with the application of planning techniques is used in order to diagnose CVDs. A combination of biological data and medical imaging has been used. Ultimately, as this is a challenging research area open issues and its possible future works have also been discussed.
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 - Proceedings |
| Editors | Vassil Sgurev, Vladimir Jotsov, Rudolf Kruse, Mincho Hadjiski |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 200-204 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-7281-5456-5 |
| ISBN (Print) | 978-1-7281-5457-2 |
| DOIs | |
| Publication status | Published - 18 Sept 2020 |
| Event | 10th IEEE International Conference on Intelligent Systems - Sofia, Bulgaria Duration: 28 Aug 2020 → 30 Aug 2020 |
Publication series
| Name | 2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 - Proceedings |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 1541-1672 |
Conference
| Conference | 10th IEEE International Conference on Intelligent Systems |
|---|---|
| Abbreviated title | IS 2020 |
| Country/Territory | Bulgaria |
| City | Sofia |
| Period | 28/08/20 → 30/08/20 |
Keywords
- cardiovascular diseases
- convolutional neural network
- deep learning
- U-Net
Access to Document
- IEEE paper Giovanah Gogi (1)
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.